An Automated Convolutional Neural Network Based Approach for Paddy Leaf Disease Detection

Bangladesh and India are significant paddy-cultivation countries in the globe. Paddy is the key producing crop in Bangladesh. In the last 11 years, the part of agriculture in Bangladesh's Gross Domestic Product (GDP) was contributing about 15.08 percent. But unfortunately, the farmers who are working so hard to grow this crop, have to face huge losses because of crop damages caused by various diseases of paddy. There are approximately more than 30 diseases of paddy leaf and among them, about 7-8 diseases are quite common in Bangladesh. Paddy leaf diseases like Brown Spot Disease, Blast Disease, Bacterial Leaf Blight, etc. are very well known and most affecting one among different paddy leaf diseases. These diseases are hampering the growth and productivity of paddy plants which can lead to great ecological and economical losses. If these diseases can be detected at an early stage with great accuracy and in a short time, then the damages to the crops can be greatly reduced and the losses of the farmers can be prevented. This paper has worked on 4 types of diseases and one healthy leaf class of the paddy. The main goal of this paper is to provide the best results for paddy leaf disease detection through an automated detection approach with the deep learning CNN models that can achieve the highest accuracy instead of the traditional lengthy manual disease detection process where the accuracy is also greatly questionable. It has analyzed four models such as VGG-19, Inception-Resnet-V2, ResNet-101, Xception, and achieved better accuracy from Inception-ResNet-V2 is 92.68%.